{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:39.251506Z",
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     "shell.execute_reply": "2025-02-07T10:54:39.253574Z",
     "shell.execute_reply.started": "2025-02-07T10:54:39.251480Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:39.255499Z",
     "iopub.status.busy": "2025-02-07T10:54:39.255203Z",
     "iopub.status.idle": "2025-02-07T10:54:39.264197Z",
     "shell.execute_reply": "2025-02-07T10:54:39.263574Z",
     "shell.execute_reply.started": "2025-02-07T10:54:39.255455Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:39.265338Z",
     "iopub.status.busy": "2025-02-07T10:54:39.264907Z",
     "iopub.status.idle": "2025-02-07T10:54:39.268055Z",
     "shell.execute_reply": "2025-02-07T10:54:39.267438Z",
     "shell.execute_reply.started": "2025-02-07T10:54:39.265304Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
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     "shell.execute_reply": "2025-02-07T10:54:39.294039Z",
     "shell.execute_reply.started": "2025-02-07T10:54:39.269262Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 1167236.80it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:39.295648Z",
     "iopub.status.busy": "2025-02-07T10:54:39.295238Z",
     "iopub.status.idle": "2025-02-07T10:54:39.303982Z",
     "shell.execute_reply": "2025-02-07T10:54:39.303483Z",
     "shell.execute_reply.started": "2025-02-07T10:54:39.295627Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
     "start_time": "2025-02-06T14:04:29.250423Z"
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     "iopub.status.busy": "2025-02-07T10:54:39.305614Z",
     "iopub.status.idle": "2025-02-07T10:54:39.308429Z",
     "shell.execute_reply": "2025-02-07T10:54:39.307916Z",
     "shell.execute_reply.started": "2025-02-07T10:54:39.306003Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:39.309322Z",
     "iopub.status.busy": "2025-02-07T10:54:39.309053Z",
     "iopub.status.idle": "2025-02-07T10:54:39.315085Z",
     "shell.execute_reply": "2025-02-07T10:54:39.314618Z",
     "shell.execute_reply.started": "2025-02-07T10:54:39.309303Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
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   "id": "c1dd026c49a90114",
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   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
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   "cell_type": "code",
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   "id": "71ae51fb9433db98",
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   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
   "cell_type": "code",
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   "id": "24e99727ef49605",
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   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
   "metadata": {
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   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:39.494349Z",
     "iopub.status.busy": "2025-02-07T10:54:39.493897Z",
     "iopub.status.idle": "2025-02-07T10:54:39.702538Z",
     "shell.execute_reply": "2025-02-07T10:54:39.701882Z",
     "shell.execute_reply.started": "2025-02-07T10:54:39.494317Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:39.703539Z",
     "iopub.status.busy": "2025-02-07T10:54:39.703260Z",
     "iopub.status.idle": "2025-02-07T10:54:39.709624Z",
     "shell.execute_reply": "2025-02-07T10:54:39.709045Z",
     "shell.execute_reply.started": "2025-02-07T10:54:39.703519Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:39.710706Z",
     "iopub.status.busy": "2025-02-07T10:54:39.710302Z",
     "iopub.status.idle": "2025-02-07T10:54:39.716665Z",
     "shell.execute_reply": "2025-02-07T10:54:39.716136Z",
     "shell.execute_reply.started": "2025-02-07T10:54:39.710682Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.4867, 0.0765, 0.4368],\n",
      "        [0.2073, 0.5582, 0.2345]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:39.717555Z",
     "iopub.status.busy": "2025-02-07T10:54:39.717308Z",
     "iopub.status.idle": "2025-02-07T10:54:39.960339Z",
     "shell.execute_reply": "2025-02-07T10:54:39.959717Z",
     "shell.execute_reply.started": "2025-02-07T10:54:39.717536Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:39.961422Z",
     "iopub.status.busy": "2025-02-07T10:54:39.961115Z",
     "iopub.status.idle": "2025-02-07T10:54:40.194335Z",
     "shell.execute_reply": "2025-02-07T10:54:40.193698Z",
     "shell.execute_reply.started": "2025-02-07T10:54:39.961399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.489814Z",
     "start_time": "2025-02-06T14:04:30.481541Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:40.195518Z",
     "iopub.status.busy": "2025-02-07T10:54:40.195187Z",
     "iopub.status.idle": "2025-02-07T10:54:40.205877Z",
     "shell.execute_reply": "2025-02-07T10:54:40.205180Z",
     "shell.execute_reply.started": "2025-02-07T10:54:40.195493Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.497908Z",
     "start_time": "2025-02-06T14:04:30.490811Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:40.206886Z",
     "iopub.status.busy": "2025-02-07T10:54:40.206671Z",
     "iopub.status.idle": "2025-02-07T10:54:40.212963Z",
     "shell.execute_reply": "2025-02-07T10:54:40.212411Z",
     "shell.execute_reply.started": "2025-02-07T10:54:40.206866Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.505801Z",
     "start_time": "2025-02-06T14:04:30.498905Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:40.213934Z",
     "iopub.status.busy": "2025-02-07T10:54:40.213707Z",
     "iopub.status.idle": "2025-02-07T10:54:40.219978Z",
     "shell.execute_reply": "2025-02-07T10:54:40.219489Z",
     "shell.execute_reply.started": "2025-02-07T10:54:40.213915Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.512835Z",
     "start_time": "2025-02-06T14:04:30.506798Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:40.222911Z",
     "iopub.status.busy": "2025-02-07T10:54:40.222661Z",
     "iopub.status.idle": "2025-02-07T10:54:40.227720Z",
     "shell.execute_reply": "2025-02-07T10:54:40.227200Z",
     "shell.execute_reply.started": "2025-02-07T10:54:40.222891Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.667599Z",
     "start_time": "2025-02-06T14:04:30.513830Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:40.228569Z",
     "iopub.status.busy": "2025-02-07T10:54:40.228332Z",
     "iopub.status.idle": "2025-02-07T10:54:40.376086Z",
     "shell.execute_reply": "2025-02-07T10:54:40.375422Z",
     "shell.execute_reply.started": "2025-02-07T10:54:40.228549Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:40.377226Z",
     "iopub.status.busy": "2025-02-07T10:54:40.376970Z",
     "iopub.status.idle": "2025-02-07T10:54:40.861630Z",
     "shell.execute_reply": "2025-02-07T10:54:40.860989Z",
     "shell.execute_reply.started": "2025-02-07T10:54:40.377203Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:40.862922Z",
     "iopub.status.busy": "2025-02-07T10:54:40.862596Z",
     "iopub.status.idle": "2025-02-07T10:54:40.881860Z",
     "shell.execute_reply": "2025-02-07T10:54:40.881035Z",
     "shell.execute_reply.started": "2025-02-07T10:54:40.862898Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:40.883406Z",
     "iopub.status.busy": "2025-02-07T10:54:40.882708Z",
     "iopub.status.idle": "2025-02-07T10:54:40.888365Z",
     "shell.execute_reply": "2025-02-07T10:54:40.887679Z",
     "shell.execute_reply.started": "2025-02-07T10:54:40.883378Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:40.889669Z",
     "iopub.status.busy": "2025-02-07T10:54:40.889086Z",
     "iopub.status.idle": "2025-02-07T10:54:40.893795Z",
     "shell.execute_reply": "2025-02-07T10:54:40.893141Z",
     "shell.execute_reply.started": "2025-02-07T10:54:40.889647Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:40.894727Z",
     "iopub.status.busy": "2025-02-07T10:54:40.894467Z",
     "iopub.status.idle": "2025-02-07T10:54:41.001073Z",
     "shell.execute_reply": "2025-02-07T10:54:41.000427Z",
     "shell.execute_reply.started": "2025-02-07T10:54:40.894706Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:41.002146Z",
     "iopub.status.busy": "2025-02-07T10:54:41.001851Z",
     "iopub.status.idle": "2025-02-07T10:54:41.006376Z",
     "shell.execute_reply": "2025-02-07T10:54:41.005835Z",
     "shell.execute_reply.started": "2025-02-07T10:54:41.002127Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:41.007466Z",
     "iopub.status.busy": "2025-02-07T10:54:41.007056Z",
     "iopub.status.idle": "2025-02-07T10:54:41.012549Z",
     "shell.execute_reply": "2025-02-07T10:54:41.012022Z",
     "shell.execute_reply.started": "2025-02-07T10:54:41.007447Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"nums_head_4.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:41.013652Z",
     "iopub.status.busy": "2025-02-07T10:54:41.013222Z",
     "iopub.status.idle": "2025-02-07T10:54:41.017628Z",
     "shell.execute_reply": "2025-02-07T10:54:41.017145Z",
     "shell.execute_reply.started": "2025-02-07T10:54:41.013633Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:41.018514Z",
     "iopub.status.busy": "2025-02-07T10:54:41.018202Z",
     "iopub.status.idle": "2025-02-07T10:54:41.022759Z",
     "shell.execute_reply": "2025-02-07T10:54:41.022310Z",
     "shell.execute_reply.started": "2025-02-07T10:54:41.018495Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:41.023746Z",
     "iopub.status.busy": "2025-02-07T10:54:41.023574Z",
     "iopub.status.idle": "2025-02-07T10:54:41.031636Z",
     "shell.execute_reply": "2025-02-07T10:54:41.031105Z",
     "shell.execute_reply.started": "2025-02-07T10:54:41.023729Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:41.032618Z",
     "iopub.status.busy": "2025-02-07T10:54:41.032329Z",
     "iopub.status.idle": "2025-02-07T10:54:41.341846Z",
     "shell.execute_reply": "2025-02-07T10:54:41.341218Z",
     "shell.execute_reply.started": "2025-02-07T10:54:41.032600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 512, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.1, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 4, # 可调整\n",
    "    \"num_decoder_layers\": 6, # 可调整\n",
    "    \"num_encoder_layers\": 6, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": False,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 2048\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:41.342874Z",
     "iopub.status.busy": "2025-02-07T10:54:41.342565Z",
     "iopub.status.idle": "2025-02-07T10:54:42.165413Z",
     "shell.execute_reply": "2025-02-07T10:54:42.164541Z",
     "shell.execute_reply.started": "2025-02-07T10:54:41.342852Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 71938239\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:54:42.166533Z",
     "iopub.status.busy": "2025-02-07T10:54:42.166224Z",
     "iopub.status.idle": "2025-02-07T11:29:10.671577Z",
     "shell.execute_reply": "2025-02-07T11:29:10.671001Z",
     "shell.execute_reply.started": "2025-02-07T10:54:42.166511Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "42499it [34:26, 20.57it/s, epoch=39, loss=2.23, val_loss=3.06]                        "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 40 / global_step 42500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T11:29:10.673222Z",
     "iopub.status.busy": "2025-02-07T11:29:10.672740Z",
     "iopub.status.idle": "2025-02-07T11:29:10.887162Z",
     "shell.execute_reply": "2025-02-07T11:29:10.886595Z",
     "shell.execute_reply.started": "2025-02-07T11:29:10.673195Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/nums_head_4.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T11:29:10.888207Z",
     "iopub.status.busy": "2025-02-07T11:29:10.887856Z",
     "iopub.status.idle": "2025-02-07T11:29:26.210989Z",
     "shell.execute_reply": "2025-02-07T11:29:26.210425Z",
     "shell.execute_reply.started": "2025-02-07T11:29:10.888186Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load test dataset from wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "8it [00:00, 72.02it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "32it [00:00, 74.20it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:14, 68.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 2.9901519759594777\n",
      "testing bleu: 0.6673354610733698\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9ab2039d24b72a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T11:29:26.212093Z",
     "iopub.status.busy": "2025-02-07T11:29:26.211679Z",
     "iopub.status.idle": "2025-02-07T11:29:26.214733Z",
     "shell.execute_reply": "2025-02-07T11:29:26.214209Z",
     "shell.execute_reply.started": "2025-02-07T11:29:26.212069Z"
    }
   },
   "outputs": [],
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7c1e0999eb365d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T11:29:26.215855Z",
     "iopub.status.busy": "2025-02-07T11:29:26.215470Z",
     "iopub.status.idle": "2025-02-07T11:29:26.943676Z",
     "shell.execute_reply": "2025-02-07T11:29:26.943004Z",
     "shell.execute_reply.started": "2025-02-07T11:29:26.215834Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5d288cf83d67b62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T11:29:26.944726Z",
     "iopub.status.busy": "2025-02-07T11:29:26.944413Z",
     "iopub.status.idle": "2025-02-07T11:29:28.151661Z",
     "shell.execute_reply": "2025-02-07T11:29:28.151009Z",
     "shell.execute_reply.started": "2025-02-07T11:29:26.944704Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading vocabulary from ./wmt16/vocab ...\n",
      "Read 762259 words (18107 unique) from vocabulary file.\n",
      "Loading codes from ./wmt16/bpe.20000 ...\n",
      "Read 20001 codes from the codes file.\n",
      "  8%|▊         | 10/128 [00:00<00:01, 61.07it/s]\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the \"figure\" keyword\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['man in a small white boat on a lake.']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_list = [\n",
    "    \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake.\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach.\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race.\n",
    "    # \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
